Introducing Algorithm of Thought: A New Way to Interact with LLMs

A tiny model with only 7 million parameters outperforms Deepseek R1, o3-mini, and Gemini 2.5 on many benchmarks In a landscape where many Large Language Models (LLMs) are becoming increasingly "enormous," the Tiny Recursive Model (TRM) draws a completely different approach: it uses a small network and recursion to perform deep reasoning - achieving high efficiency with a modest parameter count (7 million parameters). On some difficult tasks, this model even performs better than LLMs that are tens of thousands of times larger. Paper link: https://lnkd.in/gURhtsEJ #AI #LLM #MachineLearning #TinyModels #Research #DeepLearning

Discover our new blog post about LLMs in Action: What Works, What Doesn’t Large Language Models (LLMs) are powerful, but not every use case delivers real value. Our new blog series “Evaluating LLM Applications” (Part 1 of 2) written by our expert Niklas Ullmann reveals practical tips, pitfalls to avoid, and strategies to make LLMs work for your business. In the next article, we’ll provide a technical deep dive into RAGAS implementation with a practical case study — showing how this framework turns LLM evaluation from an art into a science, with concrete examples for technical teams. 🔗 Read Part 1 now: https://lnkd.in/d-FdjF5X #AI #LLM #LargeLanguageModels #MachineLearning #DataDriven

AI Agents from First Principles Large language models (LLMs) are improving quickly. These models can understand and generate text with a high level of skill. As they become more powerful, we can use them to build systems that handle harder tasks, connect with the outside world, and work over longer periods of time. These systems are called AI agents. Full blog post: https://lnkd.in/gy_HkX2h #GenAI  #AI  #LLM  #PromptEngineering  #ContextEngineering #LangGraph

No new top stories about AI or large language models appeared on Hacker News in the past day. To find updates, try changing your search terms or look further back in time. Stay flexible when searching for recent insights on AI topics. - What are the latest AI headlines on Hacker News? - How often does Hacker News feature LLM news? - How can you find current AI trends if no news appears? #ai #artificialintelligence #ainews #CarolEstrano #aiavatar

“At this point, it is worth taking a step back and reflecting on what we mean by thinking and consciousness.” In our latest Expert Insights feature, CAN.B Group Advisory Board Member Dr Rajiv Shah explores what it really means for machines to ‘think’ and why the answer matters for Australia’s digital future. It’s a thought-provoking read that cuts through hype and focuses on the deeper questions shaping how AI and human decision-making intersect. #ExpertInsights #AI #SovereignCapability #MissionOverMoney #AUSOVRN #CANBGroup #EmpowerInnovateSimplify https://lnkd.in/gnmAny-z

Evaluating language model answers can be challenging, especially without labeled data. When labeled data is available, metrics like cosine similarity and ROUGE score can be used to compare model answers to human answers. However, in many industry use cases, such labeled data is not available. Metrics like RAGAS, which measures the faithfulness of large language models, and approaches like LM eval, where a model is asked to evaluate its own answers, can be helpful in these situations. These methods help gauge answer similarity and relevance. #languagemodels #AI #evaluation #RAGAS #LMeval

Retrieval augmented generation (RAG) takes AI to the next level. Linking large language models (LLMs) with external knowledge helps generate smarter, more relevant, and higher-quality answers. However, not all databases are built with RAG in mind...🤔 Check out our latest infographic and discover six key factors to help you make a smart, scalable RAG database choice for your business.⬇️ https://lnkd.in/d_bv3S9G #RAG #ai #GenAI #distributedsql #yugabytedb #Vector

How do LLMs actually work? At their core, Large Language Models are just giant pattern recognizers trained on massive amounts of text. They learn the structure of language and predict the next word in a sequence. But behind that simple idea is a fascinating stack of layers. Embeddings – Words are turned into numbers, capturing meaning in multi-dimensional space. Attention Mechanism – The model scans all words in a sentence and decides which ones matter most for the next prediction. Transformer Layers – Dozens (or even hundreds) of layers refine these relationships, allowing the model to handle context over thousands of tokens. Output – The system generates the most likely next word (or sequence of words) based on everything it has seen. Instead of just reading about it abstractly, visualizations make this process click. You can literally watch input text flow through embeddings, attention maps, and layers until it transforms into coherent output. 👉 Which stage of an LLM’s process would you like to see illustrated? Credits: Akshay Pachaar #AI #LLM #MachineLearning

How Large Language Models (LLMs) actually work In the first article of this AI series, Why AI struggles with “no” and what that teaches us about ourselves, we explored how language models often misinterpret negation and how those mistakes mirror some very human learning patterns. In this new piece, we’re going a step further, trying to draw a clear line between human and machine intelligence while answering the following: Are today’s language models really intelligent in the way we are? Do they reason, reflect, and create from scratch? The short answer? No. Language models can sound equally inspired, but their creativity is different. They don’t create from meaning; they predict patterns. When you see an impressive AI-generated paragraph, what you’re reading is the result of billions of statistical predictions that mimic the structure of human expression. The illusion feels real because prediction and creativity share a similar surface: both produce something new from something known. That difference between meaning and mimicry is https://lnkd.in/gVVB9ac7

Evaluating large language models (LLMs) often relies on subjective human evaluation. Experts manually assess outputs, tweaking prompts based on perceived accuracy. However, objective benchmarks like FinanceBench and RAG-Instruct offer labeled datasets for cosine similarity or BLEU score analysis, enabling string and chunk matching. The challenge arises with unstructured real-world data, making it difficult to gauge answer correctness without human oversight. The key is to move beyond LLM evaluation and incorporate human review for reliable assessment. #LLMs #FinanceBench #RAGInstruct #AI #DataAnalysis